Triple
T7223828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delmas |
E150326
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Carrefour |
E150325
|
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: Carrefour | Statement: [Delmas, borders, Carrefour]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carrefour Context triple: [Delmas, borders, Carrefour]
-
A.
Carrefour
Carrefour is a major French multinational retail corporation and one of the world’s largest hypermarket chains.
-
B.
Carrefour
chosen
Carrefour is a major suburban city in Haiti, located just southwest of the capital Port-au-Prince and known as part of the country's largest metropolitan area.
-
C.
Kaufhof
Kaufhof was a major German department store chain that later became part of the Galeria Kaufhof brand.
-
D.
Tesco
Tesco is a major British multinational grocery and general merchandise retailer and one of the largest supermarket chains in the world.
-
E.
Carrefour Laval
Carrefour Laval is a major shopping mall in Laval, Quebec, featuring a wide range of retail stores, restaurants, and entertainment options.
- 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_69c687effb44819092b95d07d0368c9f |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e9db51888190b8463d0003f334fa |
completed | March 27, 2026, 8:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cc11b48881909886a71f1b887789 |
completed | March 28, 2026, 12:39 p.m. |
Created at: March 27, 2026, 2:54 p.m.