Triple
T12233209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marquesses of Buckingham |
E291525
|
entity |
| Predicate | familyNameAssociated |
P18
|
FINISHED |
| Object | Temple |
E436164
|
NE FINISHED |
How this triple was built (2 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: Temple | Statement: [Marquesses of Buckingham, familyNameAssociated, Temple]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Temple Context triple: [Marquesses of Buckingham, familyNameAssociated, Temple]
-
A.
Temple
Temple is a mid-sized city in central Texas known as a regional medical and transportation hub.
-
B.
Temple
chosen
Temple is a surname of English origin borne by various notable individuals across fields such as politics, sports, and the arts.
-
C.
Temple
Temple is a Paris Métro station in central Paris, serving line 3 near the Place de la République.
-
D.
Temple
Temple is a historic legal district in central London, renowned for housing the Inns of Court and many barristers’ chambers.
-
E.
Temple
Temple is the athletic program of Temple University, best known for its NCAA Division I sports teams, particularly in basketball and football.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91ca5a06481908c7c6b715b9f6713 |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e5e32908190a3c1e75ba336ad89 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:51 p.m.