Triple
T177298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Albert J. Weatherhead III University Professor at Harvard University |
E3600
|
entity |
| Predicate | holderRank |
P1944
|
FINISHED |
| Object | professor |
—
|
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: professor | Statement: [Albert J. Weatherhead III University Professor at Harvard University, holderRank, professor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: holderRank Context triple: [Albert J. Weatherhead III University Professor at Harvard University, holderRank, professor]
-
A.
highestRankIn
Indicates that one entity holds the top or most senior rank within a specified group, category, or context relative to other entities.
-
B.
honorLevel
Indicates the degree or status of respect, distinction, or recognition accorded to an entity relative to others.
-
C.
rankedBy
Indicates that one entity is ordered or assigned a position in a hierarchy or list according to criteria determined or applied by another entity.
-
D.
honorificRank
Indicates that one entity holds a formal title or honorific status in relation to another entity.
-
E.
rankedAs
chosen
Indicates that one entity is assigned a specific position or level in an ordered ranking relative to others.
- 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_69a25374990081909766d30c79a18e0e |
completed | Feb. 28, 2026, 2:31 a.m. |
| NER | Named-entity recognition | batch_69a258fd278481908ad4498e03f38e2f |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a2566b53d481909c0ed40dd3719e8c |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:39 a.m.