Triple
T5455506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diocese of Saint-Denis |
E122467
|
entity |
| Predicate | coversMunicipality |
P8465
|
FINISHED |
| Object | Stains |
E245374
|
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: Stains | Statement: [Diocese of Saint-Denis, coversMunicipality, Stains]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stains Context triple: [Diocese of Saint-Denis, coversMunicipality, Stains]
-
A.
Stains
chosen
Stains is a suburban commune in the northern outskirts of Paris, France, known for its residential neighborhoods and diverse population.
-
B.
Tinge
Tinge is a figure from Greek mythology known primarily as the wife of the Libyan giant Antaeus.
-
C.
Tinte
Tinte is a small village in the Dutch province of South Holland, known for its rural character and annual local festivities.
-
D.
Sticks & Stones
Sticks & Stones is a 2019 stand-up comedy special by Dave Chappelle known for its provocative, boundary-pushing material and controversial social commentary.
-
E.
Stikker
Stikker is a surname most notably associated with Dirk Stikker, a Dutch politician and diplomat who served as NATO Secretary General.
- 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_69bd46424248819085282ddf50a565f3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd964583008190bf7b94f656e4ecf2 |
completed | March 20, 2026, 6:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf41433abc8190998bdb0fa8b18041 |
completed | March 22, 2026, 1:09 a.m. |
Created at: March 20, 2026, 2:08 p.m.