Triple
T37468475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anthony Frank Hawk |
E931091
|
entity |
| Predicate | hasVideoGameSeries |
P14856
|
FINISHED |
| Object | Tony Hawk's Pro Skater |
—
|
NE NERFINISHED |
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: Tony Hawk's Pro Skater | Statement: [Anthony Frank Hawk, hasVideoGameSeries, Tony Hawk's Pro Skater]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVideoGameSeries Context triple: [Anthony Frank Hawk, hasVideoGameSeries, Tony Hawk's Pro Skater]
-
A.
hasVideoGames
Indicates that one entity possesses, owns, or includes video games in relation to another entity or context.
-
B.
seriesGame
chosen
Indicates that a game is part of, or belongs to, a particular series or franchise.
-
C.
hasVideoGameTieIn
Indicates that one entity (such as a work, product, or franchise) has an associated or derived video game adaptation or related game-based product.
-
D.
hasFictionalSeries
Indicates that one entity is a fictional series that another entity possesses, is associated with, or is the creator/owner of.
-
E.
hasGenreInVideoGameAdaptations
Indicates that a work is associated with a specific genre specifically in the context of its video game adaptations.
- 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_69f76ec2af148190897d101070d7f415 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fdb04ed81c8190b8feea90c1c785a6 |
completed | May 8, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69fda9d6c5148190a63205b6d9b0a1b4 |
completed | May 8, 2026, 9:16 a.m. |
Created at: May 3, 2026, 4:17 p.m.