Triple
T31320663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fujifilm X-T10 |
E798726
|
entity |
| Predicate | numberOfAFPoints |
P170115
|
FINISHED |
| Object | 77 |
—
|
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: 77 | Statement: [Fujifilm X-T10, numberOfAFPoints, 77]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAFPoints Context triple: [Fujifilm X-T10, numberOfAFPoints, 77]
-
A.
afPoints
chosen
Indicates a relationship where a scoring or point value is assigned, tracked, or associated with an entity or interaction.
-
B.
totalPointsAllowed
Indicates the total number of points that an entity (such as a team, player, or defense) has allowed opponents to score over a specified period or set of events.
-
C.
touchdownPoints
Indicates the number of points awarded to a team for successfully scoring a touchdown in a game.
-
D.
pointsForWin
Indicates the number of points awarded to an entity for achieving a win in a given context or competition.
-
E.
totalPointsAvailable
Indicates the complete number of points that can be obtained or assigned within a given context or activity.
- F. None of above.
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_69f224e3238c8190b2291f50ea4962cd |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a00d08e8fac8190b59359134e6e1c03 |
completed | May 10, 2026, 6:38 p.m. |
| PD | Predicate disambiguation | batch_6a00d0127c088190a6f5b360450af113 |
completed | May 10, 2026, 6:36 p.m. |
Created at: April 29, 2026, 9:15 p.m.