Triple
T3997943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TGV Sud-Est |
E87142
|
entity |
| Predicate | numberOfIntermediateCoaches |
P53409
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [TGV Sud-Est, numberOfIntermediateCoaches, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfIntermediateCoaches Context triple: [TGV Sud-Est, numberOfIntermediateCoaches, 8]
-
A.
numberOfCoaches
Indicates the total count of coaches associated with a given entity or context.
-
B.
numberOfIntermediateStops
Indicates the count of stops or pauses that occur between the starting point and the final destination in a journey or process.
-
C.
hasIntermediateStation
Indicates that a route, journey, or connection includes a station that lies between its starting point and its final destination.
-
D.
hasIntermediateCity
Indicates that there is a city located between two other places along a route or connection.
-
E.
trainCount
Indicates the number of trains associated with a given entity, context, or time period.
- 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_69aed94118148190975e6aa4e554cde9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa8579288190940487ad07e38de0 |
completed | March 9, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69aef8f89f2881909b0965419d15d46c |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefa815f2c8190818c9ffd9d1bf478 |
completed | March 9, 2026, 4:51 p.m. |
Created at: March 9, 2026, 3:34 p.m.