Triple
T11441028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zaragoza-Delicias station |
E271142
|
entity |
| Predicate | servedBy |
P82
|
FINISHED |
| Object | AVE |
E590178
|
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: AVE | Statement: [Zaragoza-Delicias station, servedBy, AVE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AVE Context triple: [Zaragoza-Delicias station, servedBy, AVE]
-
A.
AVE
chosen
AVE is Spain’s high-speed rail service, connecting major cities like Madrid and Barcelona with fast, long-distance trains.
-
B.
AV
AV is the Italian vehicle registration code assigned to the province of Avellino in the Campania region.
-
C.
AV
AV is the two-letter IATA airline designator assigned to Avianca, the flag carrier of Colombia and one of Latin America’s largest airlines.
-
D.
ATE
ATE is a U.S. National Science Foundation program that supports the education and training of technicians for advanced technology fields through partnerships between two-year colleges, industry, and other educational institutions.
-
E.
ALE
ALE is a widely used research platform that provides a common interface to hundreds of Atari 2600 games for developing and evaluating artificial intelligence and reinforcement learning algorithms.
- 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d80888190c8190b6365550ffe4931c |
completed | April 9, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5d3a2a68481909704ef9a7f780afc |
completed | April 20, 2026, 7:20 a.m. |
Created at: April 8, 2026, 9:35 p.m.