Triple
T25052233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hirving Lozano |
E627416
|
entity |
| Predicate | wearsNumberForNationalTeam |
P156847
|
FINISHED |
| Object | 22 |
—
|
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: 22 | Statement: [Hirving Lozano, wearsNumberForNationalTeam, 22]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wearsNumberForNationalTeam Context triple: [Hirving Lozano, wearsNumberForNationalTeam, 22]
-
A.
playsForNationalTeamShirtNumber
chosen
Indicates the shirt number a player wears when representing their national team in official matches.
-
B.
usedByNationalTeamSince
Indicates that something has been utilized or employed by a national team starting from a specific point in time.
-
C.
associatedWithNationalTeam
Indicates that an entity has a formal connection or involvement with a specific national team, such as playing for, coaching, or otherwise being officially linked to it.
-
D.
playedForNationalTeamFrom
Indicates that an entity was a member of and played for a particular national team starting from a specified date or time period.
-
E.
representedNationalTeamInCompetition
Indicates that an individual was a member of and competed for a specific national team in a particular competition.
- 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_69e2ff2c45f48190afa28369f1df6786 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f454a379488190a87935a19cfac26e |
completed | May 1, 2026, 7:22 a.m. |
| PD | Predicate disambiguation | batch_69f442c861188190967655c6d8012380 |
completed | May 1, 2026, 6:06 a.m. |
Created at: April 18, 2026, 6:09 a.m.