Triple
T195167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MLS Cup 2005 |
E3802
|
entity |
| Predicate | titleCountForLosAngelesGalaxy |
P4419
|
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: [MLS Cup 2005, titleCountForLosAngelesGalaxy, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: titleCountForLosAngelesGalaxy Context triple: [MLS Cup 2005, titleCountForLosAngelesGalaxy, 2]
-
A.
titleCount
chosen
Indicates the number of distinct titles associated with an entity within a given context.
-
B.
starCount
Indicates the number of stars associated with an entity, typically representing a rating, quality level, or count of starred items.
-
C.
starCountAboveEagle
Indicates that the number of stars associated with one entity is greater than the number of stars associated with the entity referred to as "Eagle."
-
D.
numberOfDomes
Indicates the quantity of domes that an entity possesses or is associated with.
-
E.
arrowCount
Indicates the number of arrows associated with or involved in a given entity or interaction.
- 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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a25969425081908e178db8ba4631c2 |
completed | Feb. 28, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69a25677da14819094cd02868fd30c83 |
completed | Feb. 28, 2026, 2:44 a.m. |
Created at: Feb. 28, 2026, 2:41 a.m.