Triple
T14342467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ESWEEK |
E355636
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
CASES
CASES is a conference focused on computer-aided design and synthesis of embedded systems, typically held as part of the ESWEEK (Embedded Systems Week) event.
|
E1094340
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: CASES | Statement: [ESWEEK, hasComponent, CASES]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CASES Context triple: [ESWEEK, hasComponent, CASES]
-
A.
CASE
CASE is the commonly used acronym for the College of Arts, Sciences & Education, an academic division encompassing a broad range of liberal arts, scientific, and educational disciplines.
-
B.
Case
Case is a common English surname borne by various notable individuals across fields such as business, politics, and the arts.
-
C.
Caso
Caso is a Spanish-language surname borne by various notable individuals, including Mexican archaeologist and anthropologist Alfonso Caso.
-
D.
Cases-de-Pène
Cases-de-Pène is a small commune in southern France’s Pyrénées-Orientales department, known for its rural setting amid vineyards and Mediterranean landscapes.
-
E.
COUR
COUR is the stock ticker symbol for Coursera, a major online learning platform offering courses, certificates, and degrees from universities and companies worldwide.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: CASES Triple: [ESWEEK, hasComponent, CASES]
Generated description
CASES is a conference focused on computer-aided design and synthesis of embedded systems, typically held as part of the ESWEEK (Embedded Systems Week) event.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CASES Target entity description: CASES is a conference focused on computer-aided design and synthesis of embedded systems, typically held as part of the ESWEEK (Embedded Systems Week) event.
-
A.
CASE
CASE is the commonly used acronym for the College of Arts, Sciences & Education, an academic division encompassing a broad range of liberal arts, scientific, and educational disciplines.
-
B.
Case
Case is a common English surname borne by various notable individuals across fields such as business, politics, and the arts.
-
C.
Caso
Caso is a Spanish-language surname borne by various notable individuals, including Mexican archaeologist and anthropologist Alfonso Caso.
-
D.
Cases-de-Pène
Cases-de-Pène is a small commune in southern France’s Pyrénées-Orientales department, known for its rural setting amid vineyards and Mediterranean landscapes.
-
E.
COUR
COUR is the stock ticker symbol for Coursera, a major online learning platform offering courses, certificates, and degrees from universities and companies worldwide.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8e87febc8190a63c668cbd0fd713 |
completed | April 14, 2026, 6:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd469d899081909103563f209dd944 |
completed | May 8, 2026, 2:12 a.m. |
| NEDg | Description generation | batch_69fd47fa764c8190b1d691f5847b7a05 |
completed | May 8, 2026, 2:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd492226888190a014b23e506ab19c |
completed | May 8, 2026, 2:23 a.m. |
Created at: April 10, 2026, 1:14 a.m.