Triple
T10015580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A64 motorway |
E199484
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Tarbes |
E109577
|
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: Tarbes | Statement: [A64 motorway, passesNear, Tarbes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarbes Context triple: [A64 motorway, passesNear, Tarbes]
-
A.
Tarbes
chosen
Tarbes is a historic city in southwestern France, serving as the capital of the Hautes-Pyrénées department at the foot of the Pyrenees.
-
B.
Bagnères-de-Bigorre
Bagnères-de-Bigorre is a spa and ski resort town in the French Pyrenees, known for its thermal baths and mountain tourism.
-
C.
Narbonne
Narbonne is a historic city in southern France known for its Roman heritage, medieval architecture, and former status as an important Mediterranean port.
-
D.
Saint-Girons
Saint-Girons is a small town in the Ariège department of southwestern France, situated in the foothills of the Pyrenees.
-
E.
Colomiers
Colomiers is a suburban city in southwestern France, known as part of the Toulouse metropolitan area and for its strong aerospace and industrial sectors.
- 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_69ca8315a1a08190ab310f25620f362b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd4ad3348190bae03cd37c787674 |
completed | April 2, 2026, 1:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d26a99971081908397f06c0ce913d0 |
completed | April 5, 2026, 1:58 p.m. |
Created at: March 30, 2026, 8:52 p.m.