Triple
T7659192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Universe 1986 |
E173459
|
entity |
| Predicate | numberOfPlacements |
P78616
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [Miss Universe 1986, numberOfPlacements, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPlacements Context triple: [Miss Universe 1986, numberOfPlacements, 10]
-
A.
numberOfPositions
Indicates the total count of distinct positions or roles associated with a given entity.
-
B.
numberOfStandingPlaces
Indicates the total count of standing-only positions or spots available in a given context (e.g., a vehicle, venue, or area).
-
C.
numberOfOpenings
Indicates the quantity of available positions, slots, or opportunities currently open in a given context.
-
D.
numberOfPlates
Indicates the quantity of plates associated with or involved in a particular entity, event, or context.
-
E.
numberOfPlays
Indicates the total count of times an item, such as a track or media asset, has been played.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7061cbc3c8190a917dd7e71214182 |
completed | March 27, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69c7015dd8fc8190bc5f52a12bd46209 |
completed | March 27, 2026, 10:14 p.m. |
| PDg | Predicate description generation | batch_69c7061b218c81909fff789ba4c10e58 |
completed | March 27, 2026, 10:35 p.m. |
Created at: March 27, 2026, 3:59 p.m.