Triple
T24873719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephanie Jayne Houghton |
E622506
|
entity |
| Predicate | hasScoredGoalsFor |
P32660
|
FINISHED |
| Object | England women's national football team |
—
|
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: England women's national football team | Statement: [Stephanie Jayne Houghton, hasScoredGoalsFor, England women's national football team]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScoredGoalsFor Context triple: [Stephanie Jayne Houghton, hasScoredGoalsFor, England women's national football team]
-
A.
hasScoredFor
Indicates that one entity has scored points, goals, or similar achievements on behalf of another entity, such as a team, organization, or side.
-
B.
scoredGoalsInFinalOf
Indicates that one entity scored one or more goals in the final match of a specified competition or event.
-
C.
scoredFor
chosen
Indicates that one entity achieved points or a score on behalf of another entity, such as a player scoring for a team.
-
D.
numberOfGoals
Indicates the total count of goals scored or achieved by an entity in a given context.
-
E.
famousGoalAgainst
Indicates that one entity is widely recognized for scoring a notable or iconic goal against another entity.
- 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_69e2fac3fdbc81909c2ec49be5743cd9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f47b865df48190bf4b6d3e9f9305e6 |
completed | May 1, 2026, 10:08 a.m. |
| PD | Predicate disambiguation | batch_69f4682c8a3c8190adbfaac99474eaaf |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 18, 2026, 5:23 a.m.