Triple
T13626673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baltimore Colts offense |
E325599
|
entity |
| Predicate | offensiveStrength |
P111358
|
FINISHED |
| Object | passing game |
—
|
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: passing game | Statement: [Baltimore Colts offense, offensiveStrength, passing game]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offensiveStrength Context triple: [Baltimore Colts offense, offensiveStrength, passing game]
-
A.
offensiveStrategy
Indicates a strategic approach focused on attacking or aggressively advancing against an opponent.
-
B.
offensiveForce
Indicates the use or application of aggressive or attacking power or violence by one entity against another.
-
C.
opponentStrength
Indicates the level or degree of power, skill, or capability possessed by an opposing party in a competitive or adversarial context.
-
D.
offensiveCharacteristic
Indicates that one entity possesses a trait, behavior, or quality that is considered insulting, disrespectful, or likely to cause offense to another entity or group.
-
E.
offensiveTackle
Indicates that an entity plays the offensive tackle position, responsible for blocking and protecting on the offensive line in a gridiron football context.
- F. None of above. chosen
Provenance (4 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc60635d08190899806fe8936f02a |
completed | April 12, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69dbbe85e1c4819095194f4b7f9f6118 |
completed | April 12, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69dbc6043e148190a2a25f929cfa35e5 |
completed | April 12, 2026, 4:19 p.m. |
Created at: April 9, 2026, 9:51 p.m.