Triple
T8688733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Expo Porte de Versailles |
E206230
|
entity |
| Predicate | rankingInEurope |
P84197
|
FINISHED |
| Object | one of the largest exhibition centres in Europe |
—
|
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: one of the largest exhibition centres in Europe | Statement: [Paris Expo Porte de Versailles, rankingInEurope, one of the largest exhibition centres in Europe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingInEurope Context triple: [Paris Expo Porte de Versailles, rankingInEurope, one of the largest exhibition centres in Europe]
-
A.
rankingInCountry
Indicates the position or level an entity holds within an ordered list specific to a particular country.
-
B.
areaRankingInEurope
Indicates the position of an entity in a size-based ranking of areas within Europe.
-
C.
rankByLengthInEurope
Indicates that entities are ordered or compared based on their length specifically within the context of Europe.
-
D.
positionOnEuro
Indicates that an entity holds a specific official role or position within the institutions or organizational structure of the European Union.
-
E.
passengerTrafficRankInEurope
Indicates the relative position of an entity in Europe based on the volume of passenger traffic it handles.
- 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_69ca835481fc819084e33d3bc883bfa6 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc57334b0c8190903a5a1784e74791 |
completed | March 31, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69cc4569f9048190b9c86b4c81103d35 |
completed | March 31, 2026, 10:06 p.m. |
| PDg | Predicate description generation | batch_69cc483f06f48190879f4702c8b4ed00 |
completed | March 31, 2026, 10:18 p.m. |
Created at: March 30, 2026, 6:33 p.m.