Triple
T37805483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fiona |
E942488
|
entity |
| Predicate | numberOfPlayableProtagonistsInGame |
P48193
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Fiona, numberOfPlayableProtagonistsInGame, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPlayableProtagonistsInGame Context triple: [Fiona, numberOfPlayableProtagonistsInGame, 2]
-
A.
numberOfPlayableCharacters
chosen
Indicates the total count of distinct characters that can be actively controlled or played by a user in a game or interactive experience.
-
B.
numberOfHumanProtagonists
Indicates the count of human characters that serve as protagonists in a given work or context.
-
C.
protagonistCount
Indicates the number of primary protagonists involved in a given narrative or work.
-
D.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
E.
numberOfHumanCharacters
Indicates the count of distinct human characters associated with a given entity or context.
- 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_69f76ee8104c8190ab17133ccd8f86e6 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1772e48190ba738c6d11b321e2 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:19 p.m.