Triple
T28700529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NHL forwards |
E729537
|
entity |
| Predicate | oftenSpecializeIn |
P140458
|
FINISHED |
| Object | scoring |
—
|
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: scoring | Statement: [NHL forwards, oftenSpecializeIn, scoring]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenSpecializeIn Context triple: [NHL forwards, oftenSpecializeIn, scoring]
-
A.
laterSpecializedIn
Indicates that an entity initially engaged in a broader or different field and subsequently focused its work or expertise in a more specific or specialized area.
-
B.
uniformSpecialty
Indicates that multiple entities share the same specific specialty, expertise, or area of focus.
-
C.
subjectSpecialization
chosen
Indicates that one subject focuses on, or has expertise in, a particular field, topic, or area of knowledge.
-
D.
marketSpecialization
Indicates a relationship where an entity focuses its activities, products, or services on serving a specific segment or niche of a broader market.
-
E.
hasSpecialist
Indicates that one entity is associated with or assigned to a specialist entity that provides expert support, service, or oversight for it.
- 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_69f043e6e9688190b6bdd6e5665498ff |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f70e8755a48190931eaa77946f9460 |
completed | May 3, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69f70abc00848190a1c3f495ef6c8dc6 |
completed | May 3, 2026, 8:43 a.m. |
Created at: April 28, 2026, 5:42 a.m.