Triple
T21705207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pol Antràs |
E535755
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Pol |
—
|
NE NERFINISHED |
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: Pol | Statement: [Pol Antràs, givenName, Pol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pol Context triple: [Pol Antràs, givenName, Pol]
-
A.
Pol
chosen
Pol is a given name and variant of Paul, used in several European languages such as Catalan and French.
-
B.
Pal
Pal is an Indian surname notably borne by Bipin Chandra Pal, a prominent nationalist leader in the Indian independence movement.
-
C.
Pal
Pal is a small mountain village in the parish of La Massana in Andorra, known for its traditional stone-and-wood architecture and nearby ski slopes.
-
D.
Pal
Pal was the male Rough Collie dog who originated the role of Lassie in the classic 1943 film "Lassie Come Home" and subsequent productions.
-
E.
Po
Po is the small, red Teletubby character known for riding a scooter and speaking in a soft, childlike voice.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46b44c0819088ab883ebd44e0e8 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efb52e4b84819095a24cc9fdca2b8a |
completed | April 27, 2026, 7:12 p.m. |
Created at: April 16, 2026, 6:46 p.m.