Triple
T13791230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | INSPE Centre-Val de Loire |
E331400
|
entity |
| Predicate | hasType |
P0
|
FINISHED |
| Object |
INSPE
INSPE is a French higher education institution dedicated to training future teachers and education professionals.
|
E1061172
|
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: INSPE | Statement: [INSPE Centre-Val de Loire, hasType, INSPE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: INSPE Context triple: [INSPE Centre-Val de Loire, hasType, INSPE]
-
A.
INSP
INSP is Mexico’s National Institute of Public Health, a leading governmental research and training institution focused on public health and epidemiology.
-
B.
INS
INS was the former U.S. federal agency responsible for administering and enforcing immigration and naturalization laws before its functions were transferred to the Department of Homeland Security.
-
C.
INNSA
INNSA is the UN/LOCODE identifier for Jawaharlal Nehru Port, a major container port near Mumbai, India.
-
D.
INSA
INSA is India’s premier national academy dedicated to promoting excellence in science and representing the country’s scientific community at national and international levels.
-
E.
INST
INST is the stock ticker symbol for Instructure, an education technology company best known for its Canvas learning management system.
- 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: INSPE Triple: [INSPE Centre-Val de Loire, hasType, INSPE]
Generated description
INSPE is a French higher education institution dedicated to training future teachers and education professionals.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: INSPE Target entity description: INSPE is a French higher education institution dedicated to training future teachers and education professionals.
-
A.
INSP
INSP is Mexico’s National Institute of Public Health, a leading governmental research and training institution focused on public health and epidemiology.
-
B.
INS
INS was the former U.S. federal agency responsible for administering and enforcing immigration and naturalization laws before its functions were transferred to the Department of Homeland Security.
-
C.
INNSA
INNSA is the UN/LOCODE identifier for Jawaharlal Nehru Port, a major container port near Mumbai, India.
-
D.
INSA
INSA is India’s premier national academy dedicated to promoting excellence in science and representing the country’s scientific community at national and international levels.
-
E.
INST
INST is the stock ticker symbol for Instructure, an education technology company best known for its Canvas learning management system.
- 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_69d81c58feb08190a77bca8bf7d6d20f |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0258a1408190a837d17c6d6a2bd4 |
completed | April 14, 2026, 9:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b0817bd88190a46e539f2ff24d83 |
completed | May 3, 2026, 8:30 p.m. |
| NEDg | Description generation | batch_69f7b14fe1cc8190b1a5f6f0e80b7e39 |
completed | May 3, 2026, 8:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7b20f67048190a641527353e3ff43 |
completed | May 3, 2026, 8:37 p.m. |
Created at: April 9, 2026, 10:11 p.m.