Triple
T2208274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grandson |
E50852
|
entity |
| Predicate | neighboringMunicipality |
P17964
|
FINISHED |
| Object |
Concise
Concise is a small municipality in the canton of Vaud in western Switzerland, situated near Lake Neuchâtel.
|
E244504
|
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: Concise | Statement: [Grandson, neighboringMunicipality, Concise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Concise Context triple: [Grandson, neighboringMunicipality, Concise]
-
A.
Shorter
Shorter is a surname of English origin borne by various notable individuals across different fields.
-
B.
K-short
K-short is the short-lived neutral kaon, a subatomic meson that decays rapidly via the weak interaction and plays a key role in studies of CP violation.
-
C.
Short Cuts
Short Cuts is a Toronto International Film Festival program showcasing a curated selection of international and Canadian short films across genres and styles.
-
D.
Short Cuts
Short Cuts is a 1993 ensemble drama film directed by Robert Altman, adapted from Raymond Carver’s short stories and known for its interwoven narratives about Los Angeles residents.
-
E.
Long
Long is a common English surname borne by numerous notable individuals across politics, arts, sports, and other fields.
- 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: Concise Triple: [Grandson, neighboringMunicipality, Concise]
Generated description
Concise is a small municipality in the canton of Vaud in western Switzerland, situated near Lake Neuchâtel.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Concise Target entity description: Concise is a small municipality in the canton of Vaud in western Switzerland, situated near Lake Neuchâtel.
-
A.
Shorter
Shorter is a surname of English origin borne by various notable individuals across different fields.
-
B.
K-short
K-short is the short-lived neutral kaon, a subatomic meson that decays rapidly via the weak interaction and plays a key role in studies of CP violation.
-
C.
Short Cuts
Short Cuts is a Toronto International Film Festival program showcasing a curated selection of international and Canadian short films across genres and styles.
-
D.
Short Cuts
Short Cuts is a 1993 ensemble drama film directed by Robert Altman, adapted from Raymond Carver’s short stories and known for its interwoven narratives about Los Angeles residents.
-
E.
Long
Long is a common English surname borne by numerous notable individuals across politics, arts, sports, and other fields.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfcd53b88190991961f103a3e09a |
completed | March 7, 2026, 6:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae654e22b48190bb40f7c61bb359b1 |
completed | March 9, 2026, 6:14 a.m. |
| NEDg | Description generation | batch_69ae6608d3ac8190923cd6a6ce7c4c89 |
completed | March 9, 2026, 6:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae66a751f881908fda164de72dac9b |
completed | March 9, 2026, 6:20 a.m. |
Created at: March 4, 2026, 7:46 p.m.