Triple
T14603968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Firefly |
E342777
|
entity |
| Predicate | hasScoreCharacteristic |
P115017
|
FINISHED |
| Object | romantic |
—
|
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: romantic | Statement: [The Firefly, hasScoreCharacteristic, romantic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScoreCharacteristic Context triple: [The Firefly, hasScoreCharacteristic, romantic]
-
A.
hasScoreRecord
Indicates that an entity is associated with a specific score entry or scoring record.
-
B.
isScoreFor
Indicates that one value represents the score or result associated with a particular entity, event, or performance.
-
C.
hasScoreSystem
Indicates that an entity uses, is governed by, or is associated with a particular scoring or rating system.
-
D.
hasScoreboard
Indicates that one entity is equipped with or associated with a scoreboard used to display scores or results.
-
E.
hasScoredFor
Indicates that one entity has scored points, goals, or similar achievements on behalf of another entity, such as a team, organization, or side.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb43a1bb48190bf520cb961f15b5e |
completed | April 14, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69de656f9f4c81909f815b6629a9ee39 |
completed | April 14, 2026, 4:03 p.m. |
| PDg | Predicate description generation | batch_69de716c17cc8190aeb85296abee85a7 |
completed | April 14, 2026, 4:55 p.m. |
Created at: April 10, 2026, 1:25 a.m.