Triple
T57627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Harvard statue |
E1140
|
entity |
| Predicate | hasQuality |
P642
|
FINISHED |
| Object | popular photo spot |
—
|
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: popular photo spot | Statement: [John Harvard statue, hasQuality, popular photo spot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasQuality Context triple: [John Harvard statue, hasQuality, popular photo spot]
-
A.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
B.
hasMaterialType
Indicates that something is composed of, made from, or characterized by a specific type of material.
-
C.
hasNotableFeature
chosen
Indicates that an entity possesses a specific characteristic, trait, or attribute that is considered significant or noteworthy.
-
D.
hasPart
Indicates that one entity is a component, segment, or constituent part of another entity.
-
E.
hasLimitation
Indicates that an entity is subject to a constraint, restriction, or boundary that limits its scope, capability, or applicability.
- 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_69a248adc5b48190aa8db9fb092fb28a |
completed | Feb. 28, 2026, 1:45 a.m. |
| NER | Named-entity recognition | batch_69a24b915c9881908c798f4dacb39f1d |
completed | Feb. 28, 2026, 1:57 a.m. |
| PD | Predicate disambiguation | batch_69a24ac6799c8190b508933acc0a4c7d |
completed | Feb. 28, 2026, 1:54 a.m. |
Created at: Feb. 28, 2026, 1:50 a.m.