Triple
T16037475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Gonzalez |
E389004
|
entity |
| Predicate | nflReceivingTouchdownsCareer |
P10747
|
FINISHED |
| Object | 111 |
—
|
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: 111 | Statement: [Tony Gonzalez, nflReceivingTouchdownsCareer, 111]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nflReceivingTouchdownsCareer Context triple: [Tony Gonzalez, nflReceivingTouchdownsCareer, 111]
-
A.
careerReceivingTouchdowns
chosen
Indicates the total number of touchdowns a player has scored by receiving the ball over the course of their entire career.
-
B.
careerPassingTouchdownsNFL
Indicates the total number of touchdown passes a player has thrown over the course of their NFL career.
-
C.
careerNFLTouchdowns
Indicates the total number of touchdowns a player has scored over the course of their NFL career.
-
D.
ledLeagueInReceivingTouchdowns
Indicates that the subject had the highest number of receiving touchdowns in the league for a given season or time period.
-
E.
careerTotalTouchdowns
Indicates the total number of touchdowns an entity has scored over the entire duration of its career.
- 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_69d86dada3808190825d5f80d72fbe88 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1ff63edb0819092cbb671967bbdcd |
completed | April 17, 2026, 9:37 a.m. |
| PD | Predicate disambiguation | batch_69e1826f34c081908005bb736f1c485d |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:56 a.m.