Triple
T2958060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gironde department |
E79979
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Langon |
E65928
|
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: Langon | Statement: [Gironde department, contains, Langon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langon Context triple: [Gironde department, contains, Langon]
-
A.
Langon
chosen
Langon is a commune in southwestern France’s Gironde department, known as a local center for wine production and river trade along the Garonne.
-
B.
Nicholaston
Nicholaston is a small coastal village on Wales’s scenic Gower Peninsula, known for its proximity to dunes, woodlands, and the popular Nicholaston Burrows and beach.
-
C.
Bladon
Bladon is a village in Oxfordshire, England, best known as the burial place of Sir Winston Churchill.
-
D.
Kierling
Kierling is a small locality in Lower Austria best known as the place where writer Franz Kafka spent his final days and died.
-
E.
Thaton
Thaton is an ancient city in southern Myanmar historically significant as a major center of the Mon kingdom and Theravada Buddhism in the region.
- 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_69ad8b1276588190a374a0b12e0f7bdf |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad992b33e081909d22a19d5064c47d |
completed | March 8, 2026, 3:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0fc8a10848190b8eec482252eb76b |
completed | March 11, 2026, 5:24 a.m. |
Created at: March 8, 2026, 2:57 p.m.