Triple
T384319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | i-lab |
E8745
|
entity |
| Predicate | geographicAreaServed |
P82
|
FINISHED |
| Object | Harvard University community |
—
|
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: Harvard University community | Statement: [i-lab, geographicAreaServed, Harvard University community]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: geographicAreaServed Context triple: [i-lab, geographicAreaServed, Harvard University community]
-
A.
areaServed
chosen
Indicates the geographic region or jurisdiction within which a service, organization, or activity is provided or applicable.
-
B.
sectorServed
Indicates the industry or economic sector that an entity primarily serves or targets with its activities, products, or services.
-
C.
capitalOfCountryServed
Indicates that a city serves as the capital of a given country.
-
D.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
-
E.
serviceAreaCharacteristic
Indicates a relationship where a service area is associated with a specific attribute or feature that characterizes 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec422b808190b6ddf747ef939151 |
completed | Feb. 28, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69a2e967d84c8190a6b647f78d95d4e4 |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.