Triple
T9431702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garrett |
E227391
|
entity |
| Predicate | hasShortForm |
P43
|
FINISHED |
| Object | Gare |
E349447
|
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: Gare | Statement: [Garrett, hasShortForm, Gare]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gare Context triple: [Garrett, hasShortForm, Gare]
-
A.
Gare
chosen
Gare is a central district of Luxembourg City known for its main railway station and busy commercial streets.
-
B.
Gare du Palais
Gare du Palais is a historic railway and bus station in Quebec City, Canada, known for its château-style architecture and role as a major regional transport hub.
-
C.
Gare d’Orange
Gare d’Orange is the main railway station serving the town of Orange in southeastern France, providing regional and intercity train connections.
-
D.
Luzianes-Gare
Luzianes-Gare is a small civil parish in the municipality of Odemira, in Portugal’s Alentejo region.
-
E.
Gare du Stade
Gare du Stade is a local railway station serving the suburb of Colombes in the northwestern outskirts of Paris, France.
- 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_69ca8437a7ac81908651de48f2d2141d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7e6059bc8190a7e98aef3caabd0b |
completed | April 1, 2026, 8:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1104033c08190a3670b017bd984d5 |
completed | April 4, 2026, 1:21 p.m. |
Created at: March 30, 2026, 7:49 p.m.