Triple
T2209859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Place Charles-de-Gaulle |
E50888
|
entity |
| Predicate | hasNumberOfConvergingAvenues |
P37485
|
FINISHED |
| Object | 12 |
—
|
LITERAL 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: 12 | Statement: [Place Charles-de-Gaulle, hasNumberOfConvergingAvenues, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfConvergingAvenues Context triple: [Place Charles-de-Gaulle, hasNumberOfConvergingAvenues, 12]
-
A.
hasNumberOfConcourses
Indicates the relationship specifying how many concourses are associated with a given entity.
-
B.
hasNumberOfEntrances
Indicates the relationship that specifies how many entrances an entity possesses.
-
C.
hasApproachRoad
Indicates that one entity is connected to or accessed by another entity via an approach road leading to it.
-
D.
hasLanes
Indicates that an entity, such as a road or pathway, is divided into one or more distinct lanes for traffic or movement.
-
E.
hasNumberOfBridges
Indicates the quantitative relationship specifying how many bridges are associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1baa0948190b07ffc347a4f714e |
completed | March 7, 2026, 6:12 a.m. |
| PD | Predicate disambiguation | batch_69abbda8a6dc8190aa855ce2d17194b1 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abc1b912c08190b9d7bc9230e49d1d |
completed | March 7, 2026, 6:12 a.m. |
Created at: March 4, 2026, 7:46 p.m.