Triple
T2926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greater Boston |
E54
|
entity |
| Predicate | knownForIndustry |
P71
|
FINISHED |
| Object | higher education |
—
|
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: higher education | Statement: [Greater Boston, knownForIndustry, higher education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: knownForIndustry Context triple: [Greater Boston, knownForIndustry, higher education]
-
A.
notableFor
Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or role.
-
B.
recognizedAs
Indicates that one entity is acknowledged or accepted as having the identity, role, status, or classification of another entity.
-
C.
alsoKnownAs
Indicates that one entity is an alternative name, alias, or designation for another entity.
-
D.
notableWork
Indicates that one entity is a significant or well-known work (such as a book, artwork, or creation) produced by another entity.
-
E.
sector
chosen
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
- 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_69a2328f0e848190ac2840eaf2d5ebd2 |
completed | Feb. 28, 2026, 12:10 a.m. |
| NER | Named-entity recognition | batch_69a2346846608190b6b40d31f1dbd685 |
completed | Feb. 28, 2026, 12:18 a.m. |
| PD | Predicate disambiguation | batch_69a233c396ec8190986608d07fb251d4 |
completed | Feb. 28, 2026, 12:16 a.m. |
Created at: Feb. 28, 2026, 12:13 a.m.